Vikash K. Mansinghka

Author

Massachusetts Institute of Technology ORCID

Also Known As

Huot, Mathieu, Mansinghka, Vikash, Mansinghka, Vikash K, Mansinghka, Vikash K., V. Mansinghka, Vikash K Mansinghka, Vikash K. Mansinghka, Vikash Mansinghka, Xueyuan Cao

147

Publications

2.4K

Citations

H-Index

106

Concepts

All Affiliations

Massachusetts Institute of Technology University of Oxford Institute of Cognitive and Brain Sciences Harvard University Harvard University Press

About

Vikash K. Mansinghka is an author at Massachusetts Institute of Technology specializing in engineering, computer science, and data science.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	15	21	1.3K
2	Management	4	4	263
3	Language Studies	3	3	572
4	Natural Sciences	3	3	155
5	Business	1	1	13

Vikash K. Mansinghka

Publications

1.9K

Citations

H-Index

Page 1

	Year	Citations
Church: a language for generative models Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, arXiv (Cornell University) EngineeringPure LispProbabilistic ComputationMarkov Chain Monte CarloGenerative System	2012	507
Reconciling intuitive physics and Newtonian mechanics for colliding objects. Adam N. Sanborn, Vikash K. Mansinghka, Thomas L. Griffiths Psychological Review Artificial IntelligenceEngineeringModel-based ReasoningCognitionUncertain Reasoning	2013	198
Declarative Probabilistic Programming with Datalog Vikash K. Mansinghka, Daniel Selsam, Yura Perov arXiv (Cornell University)	2014	180
Picture: A probabilistic programming language for scene perception Tejas D. Kulkarni, Pushmeet Kohli, Joshua B. Tenenbaum,	2015	150
Gen: a general-purpose probabilistic programming system with programmable inference Marco Cusumano-Towner, Feras A. Saad, Alexander K. Lew, Artificial IntelligenceEngineeringMachine Learning3D Pose EstimationInference Library	2019	124
Intuitive Theories of Mind: A Rational Approach to False Belief Noah D. Goodman, Chris L. Baker, Vikash K. Mansinghka,	2005	93
Brain-wide representations of behavior spanning multiple timescales and states in C. elegans Adam A. Atanas, Jung Soo Kim, Ziyu Wang, Cell	2023	80
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs Vikash K. Mansinghka, Tejas D. Kulkarni, Yura Perov, arXiv (Cornell University) Image AnalysisMachine LearningData ScienceMachine VisionBayesian Inverse Problem	2013	55
Elements of a stochastic 3D prediction engine in larval zebrafish prey capture Andrew D. Bolton, Martin Haesemeyer, Josua Jordi, eLife EngineeringMachine LearningComputational PrinciplesPredator-prey InteractionZebrafish Combines Position	2019	49
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought Lionel Wong, Gabriel Grand, Alexander K. Lew, arXiv (Cornell University) Artificial IntelligenceEngineeringWord ModelsLexical SemanticsSemantics	2023	36

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